A Novel Algorithm for Adaptive Data Stream Clustering

In recent years, processing and management of data streams has become a topic of active research in several fields of computer science. A data stream is continuously increasing sequence of time stamped data. There are various applications in which data streams are produced such as network monitoring, telecommunication systems, stock markets, customer click streams or any type of multi-sensor system. Due to large number of data stream applications, its clustering has become an important technique in data mining and knowledge discovery. STREAM is a data stream clustering algorithm which divides data into chunks, cluster the chunks and, then, again cluster the obtained centers. An important constraint of STREAM is inadaptability with evolving data stream. Particularly it is not sensitive to evolution of the underlying data stream. In many cases, the patterns in the underlying stream may evolve and change significantly. Therefore, it is critical for the clustering process to be adaptable with such changes and provide insights over different time horizons. In this paper we have proposed an improved STREAM clustering method which retains the STREAM algorithm adaptive to drifts by adjusting itself, as the data stream changes.

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